Task Completion Research Articles

Developing an optimization model for minimizing musculoskeletal stress in repetitive motion tasks

Repetitive motion tasks are widely prevalent in various industries, including manufacturing and office environments, often leading to significant musculoskeletal stress and associated injuries. The continuous nature of these tasks, coupled with improper posture, excessive force exertion, and inadequate rest periods, exacerbates the risk of long-term damage to muscles, joints, and tendons. This paper presents a novel approach to minimizing musculoskeletal stress by developing a Reinforcement Learning (RL)—based optimization model. The model dynamically adjusts real-time task parameters, such as posture, speed, and force exertion, to reduce joint load, muscle activation, and cumulative fatigue while maintaining task performance and productivity. Data was collected from 45 participants performing repetitive tasks in a controlled laboratory environment. Key biomechanical factors, including joint load, muscle activation, and cumulative fatigue, were measured using motion capture, electromyography (EMG), and force plate systems. The RL was trained and validated using this data, with significant improvements observed across all key metrics. The results demonstrated that the model achieved an average reduction of 25%–28% in joint load, 23%–29% in muscle activation, and 26%–28% in cumulative fatigue. In addition, task completion times and accuracy were maintained or improved, demonstrating the model’s effectiveness in balancing ergonomic benefits with productivity. This study provides an integrated approach to reducing musculoskeletal stress while ensuring task efficiency, offering a dynamic, data-driven solution that can be applied across various industries. The findings suggest that RL optimization can significantly improve worker health and task sustainability without compromising organizational performance.

SKEL-Betweener: a Neural Motion Rig for Interactive Motion Authoring

Authoring 3D motions is a laborious process that requires manipulating and coordinating many control handles over time. Neural motion representations learned from large motion datasets have recently shown impressive capabilities in many motion completion tasks. However, current methods are not designed for interactive motion authoring workflows. The reasons being their requirement of a dense context of full poses, which takes considerable time to author, as well as their lack of joint-level controls for refinement. In this paper, we introduce a Neural Motion Rig called SKEL-Betweener, tailored to interactive motion authoring. SKEL-Betweener is able to generate long motion sequences from two poses only, and enables intermediate motion authoring via neural motion curves---intuitive joint-level controls for positions and orientations. Through user evaluations, we demonstrate the effectiveness of our Neural Motion Rig for efficiently creating and editing motions.

Building a better lawyer: Experimental evidence that artificial intelligence can increase legal work efficiency

AbstractRapidly improving artificial intelligence (AI) technologies have created opportunities for human–machine cooperation in legal practice. We provide evidence from an experiment with law students (N = 206) on the causal impact of machine assistance on the efficiency of legal task completion in a private law setting with natural language inputs and multidimensional AI outputs. We tested two forms of machine assistance: AI‐generated summaries of legal complaints and AI‐generated text highlighting within those complaints. AI‐generated highlighting reduced task completion time by 30% without any reduction in measured quality indicators compared to no AI assistance. AI‐generated summaries produced no change in performance metrics. AI summaries and AI highlighting together improved efficiency but not as much as AI highlighting alone. Our results show that AI support can dramatically increase the efficiency of legal task completion, but finding the optimal form of AI assistance is a fine‐tuning exercise. Currently, AI‐generated highlighting is not readily available from state‐of‐the‐art, consumer‐facing large language models, but our work suggests that this capability should be prioritized in the development of legal AI products.

Unveiling Leadership Styles in Work Life Balance: Insights from a Systematic Literature Review

The current study analyzed various leadership styles in the context of their impact on work-life balance (WLB) in organizational contexts. In this regard, a systematic literature review was conducted, where 19 peer-reviewed articles were analyzed, and published from 2003 to 2020. Whereas, relevant literature was identified through a systematic search on databases, such as Google Scholar, JSTOR, and PubMed, while utilizing keywords like 'leadership styles' and 'work-life balance.' It was revealed in the findings that transformational leadership is the most effective approach to cultivating a healthy work-life atmosphere while improving satisfaction and engagement. Whereas, Transactional leadership, emphasizes task completion and rewards, and is less promising for work-life balance, favoring organizational objectives over individual well-being frequently. Moreover, Laissez-faire leadership, known as limited direction, is connected with lower satisfaction and commitment. Consequently, the study emphasized gender-based research to handle the distinct work-life balance challenges, encountered by both men and women. It is also recommended to focus on leadership dynamics specific to organizations, especially in areas where work-life balance is vital. Additionally, essential policy proposals encompass the advancement of transformational leadership, gender-centric research, and adaptable work policies. Whereas, this review contributes to understanding the vital role leadership plays in enhancing employee well-being and organizational success.

A Local-level App vs A National-level App: Evaluation of M-Grocery Apps From The UX and The Service Performance Perspective

In Indonesia, the increase in internet users, reaching 215.63 million in 2022-2023, has facilitated the growth of mobile shopping applications like Sayurbox and Beceran, which cater to daily needs such as groceries and fresh food items. Despite receiving high user ratings, these applications face technical issues that could impact user satisfaction and company revenue. This study aims to evaluate the service quality and user experience of the Sayurbox and Beceran applications using the M-S-QUAL and UX Benchmarking methods. The M-S-QUAL method assesses service quality across nine dimensions, while UX Benchmarking evaluates user satisfaction, ease of use, error counts, success rates, and task completion times. The results of UX Benchmarking reveal that Sayurbox has fewer errors and higher user satisfaction compared to Beceran, which has faster loading times but more errors. The M-S-QUAL analysis indicates that both applications have negative gap values in most service attributes, with Sayurbox performing slightly better than Beceran. Sayurbox received a score of -0.25 for the value gap on the M-S-QUAL scale, while Beceran received a score of -0.41 for the value gap.

Morphological awareness and reading comprehension: to what extent do semantic relations in the classic sentence completion task influence associations?

AbstractIt is well-established that morphological awareness is related to reading comprehension. Morphological awareness is often assessed with a sentence completion task, in which children are asked to complete a sentence with a related word (e.g., “warm. He chose the jacket for its __”). As evident from this classic example, semantic relations could influence performance because warmth is related in meaning to jacket. We examine whether the degree of semantic relations in the sentence completion task influences the association between morphological awareness and reading comprehension. In grade 3, English-speaking children did a sentence completion task in two conditions: one with sentences designed to have high semantic relations with the target and another with low. Children also completed control measures of non-verbal reasoning, vocabulary, phonological awareness, working memory, and word reading fluency. At grade 4, children completed reading comprehension. Hierarchical regression analyses showed that performance on both conditions of the sentence completion task (i.e., high and low semantic relations) significantly predicted reading comprehension, after all controls. Intriguingly, when both tasks were in the same regression, only performance on the high semantic relations task made a unique contribution to reading comprehension. The findings confirm the contribution to reading comprehension of morphological awareness, assessed with the sentence completion task, and show the relevance of semantic dimensions to these relations. As such, findings appear to validate the use of sentence completion to assess morphological awareness and highlight its capture of the multidimensional nature of morphological awareness, including its semantic dimensions.

A Framework for Simultaneous Task Allocation and Planning under Uncertainty

We present novel techniques for simultaneous task allocation and planning in multi-robot systems operating under uncertainty. By performing task allocation and planning simultaneously, allocations are informed by individual robot behaviour, creating more efficient team behaviour. We go beyond existing work by planning for task reallocation across the team given a model of partial task satisfaction under potential robot failures and uncertain action outcomes. We model the problem using Markov decision processes, with tasks encoded in co-safe linear temporal logic, and optimise for the expected number of tasks completed by the team. To avoid the inherent complexity of joint models, we propose an alternative model that simultaneously considers task allocation and planning, but in a sequential fashion. We then build a joint policy from the sequential policy obtained from our model, thus allowing for concurrent policy execution. Furthermore, to enable adaptation in the case of robot failures, we consider replanning from failure states and propose an approach to preemptively replan in an anytime fashion, replanning for more probable failure states first. Our method also allows us to quantify the performance of the team by providing an analysis of properties, such as the expected number of completed tasks under concurrent policy execution. We implement and extensively evaluate our approach on a range of scenarios. We compare its performance to a state-of-the-art baseline in decoupled task allocation and planning: sequential single-item auctions. Our approach outperforms the baseline in terms of computation time and the number of times replanning is required on robot failure.

Anticipating Directness Strategies in Requests by Moroccan and Norwegian Speakers of English

Based on Brown and Levinson’s Weightiness Formula of face-threatening acts, this study investigates the feasibility of anticipating the degree of (in)directness used by ESL and EFL speakers when making requests in critical incidents. Furthermore, the study also compares the similarities and differences in (in)directness strategies between Moroccans and Norwegians when requesting in English. To meet these objectives, 30 Moroccan and 30 Norwegian English speakers completed an open-ended Written Discourse Completion Task. The request situations were analyzed using Brown and Levinson’s (1987) weightiness formula, whereas the requests were categorized based on Blum-Kulka’s (1984) requesting categories. The findings reveal that it is not possible to predict (in)directness when requesting in critical incidents. The analysis also suggests that Norwegian ESL speakers tend to make more indirect requests than Moroccan EFL speakers in critical incidents. By highlighting these variations, the study provides valuable insights into the impact of linguistic and cultural backgrounds on (in)directness strategies.

Disability and Patient-Reported Satisfaction in Women with Idiopathic Intracranial Hypertension: A Comparative Study of Venous Sinus Stenting and Medical Management.

Patients with chronic idiopathic intracranial hypertension (IIH) commonly experience a high level of disability and low satisfaction with medical treatment. We aim to evaluate long-term functional improvement and patient satisfaction in IIH patients with similar symptoms by comparing venous sinus stenting (VSS) to standard medical therapy. We conducted a cross-sectional questionnaire study of 111 IIH patients, comparing 37 adult female patients who underwent venous sinus stenting with 74 patients treated medically. Propensity score matching was used to balance age and presence of papilledema at presentation between groups. Headache-related disability was evaluated using the Migraine Disability Assessment Scale (MIDAS), while general function and treatment satisfaction were assessed using custom questionnaires. Electronic medical records and the results of imaging upon diagnosis were reviewed retrospectively. The stented group reported significantly better outcomes in physical well-being (median 4.0 vs. 1.0, p < 0.001), task completion (4.0 vs. 1.0, p < 0.001), work/school persistence (5.0 vs. 1.0, p < 0.001), and mental well-being (4.0 vs. 1.0, p < 0.001). Additionally, the stented group had a lower proportion of patients with severe MIDAS (MIDAS > 4, 24.3% vs. 47.9%, p = 0.017). Logistic regression suggested venous stenting as a protective factor against severe MIDAS scores (OR = 0.174, p = 0.004). Cerebral venous stenting in patients with IIH is associated with lower disability and higher patient satisfaction from medical treatment compared to those treated with medications only. These findings suggest that venous sinus stenting may be a valuable treatment option for selected IIH patients. However, larger prospective studies are needed to further validate our results.

Effect of an online training intervention on evidence-based practice in clinical nurses. #Evidencer Project

BackgroundOnline learning is becoming increasingly essential for health professionals, and it is necessary to understand how this modality affects clinical nurses' learning of Evidence-Based Practice (EBP). For this reason, the present study sought to assess the effectiveness of an online training program in improving nurses' EBP competence.MethodsA quasi-experimental study with a pretest–posttest design was conducted with a control group and without randomization for a period of 6 months. The intervention was an online self-learning course based on Melnyk’s seven steps of Evidence-Based Practice (EBP). The course was structured into sequential modules requiring 72 h of work, with task completions and tests necessary for progression. Participants had three months to complete the course. EBP competence was measured with the EBP-COQ Prof© questionnaire. Data analysis included percentages, means, standard deviations, chi-square tests, student's t-tests, and a two-way repeated measures analysis of covariance (ANCOVA).ResultsThe analysis of the changes observed in each dimension and the overall EBP competence between the intervention group (IG) and the control group (CG) showed a significant group*time interaction in three of the four dimensions of the questionnaire. The results indicated that the online Evidence-Based Practice (EBP) course significantly improved knowledge, skills, and utilization three months after its completion. In the IG, the mean score was 44.04 (standard deviation (SD) = 7), compared to 37.83 (SD = 8.5) in the CG (p < 0.001). Regarding skills, the IG had a mean score of 24.24 (SD = 3.8), while the CG scored 23.01 (SD = 3.1) (p = 0.008). For utilization, the mean score in the IG was 36.77 (SD = 6.8), and the CG was 33.12 (SD = 6.3) (p = 0.005). Overall competence also showed a statistically significant difference, with the IG achieving a mean score of 141.22 (SD = 20.0) compared to the CG with a mean score of 130.34 (SD = 16.7) (p < 0.001).ConclusionsTraining through an online education platform for three months (72 h) is an effective tool for improving the competence in Evidence-Based Practice (EBP) of clinical nurses. A significant increase was observed in knowledge and moderate improvements in skills and the application of EBP. These online courses, adapted to the needs of professionals, can be an efficient way to prepare nurses and improve their application of EBP in a clinical setting.

How does it end? Endpoints of boundaries lead to completion in macro-events.

While watching someone kicking a ball, missing moments of ball contact can be incorrectly identified as seen if the event is continued in a causal manner (i.e., the ball flying off). Does event completion also occur for events of a larger scale such as having breakfast (macro-event), which consists of multiple sub-steps like toasting bread (micro-event)? We conducted two experiments to measure event completion in macro-events presenting portions of multiple micro-events. In Experiment 1, video summaries were formed with or without event boundary information where a macro-end was either present or absent. Macro-end signified an overarching goal achievement that signaled the completion of previous tasks (such as leaving the kitchen with a full breakfast plate). More completion occurred for summaries with event boundary information and macro-ends. In Experiment 2, we tested two alternative hypotheses to explore the underlying process by showing the beginnings or ends of a micro-event. While the predictive processing hypothesis suggests that event completion is based more on predicting the future states of the event based on beginning information, the backward inferences hypothesis suggests that event completion relies more on deductions formed after the fact based on event endings. Results of Experiment 2 suggest that the ends of event boundaries lead to more event completion, possibly due to their role in forming causal connectivity. These results help to further understand event completion on a macro level.

Comparison of Occupational Performance in Immersive Virtual and Real Environments Among Patients With Stroke: Observational Randomized Crossover Pilot Study.

Conventional rehabilitation approaches involve therapists simulating various occupational tasks in health care settings or recreating real-life situations to assess and train patients in instrumental activities of daily living (IADLs). As an alternative, immersive virtual reality (IVR) has been widely used in stroke rehabilitation for years, but research comparing occupational performance between virtual and real environments is limited. This study aims to introduce a novel IVR shopping system designed for patients with stroke and to investigate the correlation of occupational performance in virtual and real environments among patients with stroke. Ten patients with stroke were recruited from the Department of Rehabilitation Medicine, Shenzhen Hospital, Southern Medical University, who met the inclusion and exclusion criteria for this observational, randomized crossover study; the patients were predominantly male (n=7), had experienced ischemic stroke (n=9), were aged 14 to 73 years, and had a time since stroke of 1 to 42 months. All patients attempted shopping tasks in virtual and real environments. The Mini-Mental State Examination (MMSE), Timed Up and Go Test (TUGT), modified Barthel index (MBI), and Lawton index (LI) were used to assess cognition, ambulation, and activities of daily living. Memory capacity and duration in the virtual and real environments were recorded as the primary parameters of occupational performance. The Wilcoxon test and Spearman correlation coefficients were used to analyze the differences and correlations between the 2 environments. The Wilcoxon test showed no significant differences between the virtual and real environments in memory capacity and duration of task completion (P>.99 and P=.99), and memory capacity in both environments correlated with the LI (ρ=0.81; P=.005). Memory duration had a relationship with the TUGT in the virtual environment (ρ=0.68; P=.03) and a borderline negative correlation with MMSE in the real environment (ρ=-0.58; P=.08). Considering the small sample size used in this study and the study's limitations, despite the significant correlation between shopping performance in IVR and the real world, it is still too early to conclude that IVR is a noninferior approach, but it presents the potential to be an alternative for assessment and training in IADLs when resources are limited. However, further research is needed to investigate the psychometric properties, clinical effects, and impact of virtual training on real-world performance. The implications for practice might include the following: (1) occupational performance in virtual shopping might be the same as real-world shopping, and more virtual IADLs could thus be developed; (2) virtual IADL assessment and training systems could be used in remote locations or locations with limited resources; and (3) more objective parameters of IADLs could be extracted from virtual environments.

A High-Concurrency Tasks Emergency Offloading Framework for Industrial Edge Networks

In the envisioned scenario of smart factories, large-scale distributed intelligent industrial devices generate a substantial number of concurrent heterogeneous industrial tasks. A single device is inadequate to meet the demanding requirements of complex tasks, which necessitate high computational capabilities and low latency in the system. Typically, multi-access edge computing (MEC) is employed to address this challenge. However, industrial edge networks face threats such as network attacks, natural disasters, and hardware failures, posing security risks to the industrial edge network. Therefore, we have designed an emergency offloading framework to enhance its resilience. Based on a task path analysis method with multiple priority levels, we developed an emergency task scheduling pre-classification (ETSPC) algorithm. Experimental results demonstrate that the proposed algorithm significantly reduces the delay of emergency offloading for high-concurrency tasks, with a maximum reduction of up to 19.2%. In extreme scenarios, the task completion rate of the ETSPC algorithm increases by 45.3% compared to the control algorithm.

An integrated optimization strategy for mission planning and control resource allocation for long-endurance unmanned aerial vehicle

PurposeWith burgeoning interest in the low-altitude economy, applications of long-endurance unmanned aerial vehicles (LE-UAVs) have increased in remote logistics distribution. Given LE-UAVs’ advantages of wide coverage, strong versatility and low cost, in addition to logistics distribution, they are widely used in military reconnaissance, communication relay, disaster monitoring and other activities. With limited autonomous intelligence, LE-UAVs require regular periodic and non-periodic control from ground control resources (GCRs) during flights and mission execution. However, the lack of GCRs significantly restricts the applications of LE-UAVs in parallel.Design/methodology/approachWe consider the constraints of GCRs, investigating an integrated optimization problem of multi-LE-UAV mission planning and GCR allocation (Multi-U&amp;G IOP). The problem integrates GCR allocation into traditional multi-UAV cooperative mission planning. The coupling decision of mission planning and GCR allocation enlarges the decision space and adds complexities to the problem’s structure. Through characterizing the problem, this study establishes a mixed integer linear programming (MILP) model for the integrated optimization problem. To solve the problem, we develop a three-stage iterative optimization algorithm combining a hybrid genetic algorithm with local search-variable neighborhood decent, heuristic conflict elimination and post-optimization of GCR allocation.FindingsNumerical experimental results show that our developed algorithm can solve the problem efficiently and exceeds the solution performance of the solver CPLEX. For small-scale instances, our algorithm can obtain optimal solutions in less time than CPLEX. For large-scale instances, our algorithm produces better results in one hour than CPLEX does. Implementing our approach allows efficient coordination of multiple UAVs, enabling faster mission completion with a minimal number of GCRs.Originality/valueDrawing on the interplay between LE-UAVs and GCRs and considering the practical applications of LE-UAVs, we propose the Multi-U&amp;G IOP problem. We formulate this problem as a MILP model aiming to minimize the maximum task completion time (makespan). Furthermore, we present a relaxation model for this problem. To efficiently address the MILP model, we develop a three-stage iterative optimization algorithm. Subsequently, we verify the efficacy of our algorithm through extensive experimentation across various scenarios.

The Impact of Transferring Embodiment and Work Efficiency Between Natural Body and Modular Body Systems

Human augmentation technology, particularly supernumerary robotic limbs, has seen rapid growth and offers promising applications. However, the cognitive aspects of supernumerary robotic limbs, such as the sense of embodiment, remain underexplored in the context of modular body systems that which provide the part of one’s own body feeling, especially when detached, and how this might impact work efficiency. This study aims to investigate the impact of experience of synchronizing Sense of Embodiment and work efficiency between the user’s innate body and a modular body system. The experiment was conducted using a modular body prototype and compared between with wearing experience (the user is more likely to perceive this robot as a part of their body) and without wearing experience (the user is more likely to perceive this robot as a separate, standalone robot). Objective evaluations included task completion time and accuracy, while the sense of embodiment questionnaire was employed for subjective evaluations. The results suggested that after having experience of wearing modular arm for a while, this experience can influence the work efficiency even if it was used without wearing it.

Two-stage deep reinforcement learning method for agile optical satellite scheduling problem

This paper investigates the agile optical satellite scheduling problem, which aims to arrange an observation sequence and observation actions for observation tasks. Existing research mainly aims to maximize the number of completed tasks or the total priorities of the completed tasks but ignores the influence of the observation actions on the imaging quality. Besides, the conventional exact methods and heuristic methods can hardly obtain a high-quality solution in a short time due to the complicated constraints and considerable solution space of this problem. Thus, this paper proposes a two-stage scheduling framework with two-stage deep reinforcement learning to address this problem. First, the scheduling process is decomposed into a task sequencing stage and an observation scheduling stage, and a mathematical model with complex constraints and two-stage optimization objectives is established to describe the problem. Then, a pointer network with a local selection mechanism and a rough pruning mechanism is constructed as the sequencing network to generate an executable task sequence in the task sequencing stage. Next, a decomposition strategy decomposes the executable task sequence into multiple sub-sequences in the observation scheduling stage, and the observation scheduling process of these sub-sequences is modeled as a concatenated Markov decision process. A neural network is designed as the observation scheduling network to determine observation actions for the sequenced tasks, which is well trained by the soft actor-critic algorithm. Finally, extensive experiments show that the proposed method, along with the designed mechanisms and strategy, is superior to comparison algorithms in terms of solution quality, generalization performance, and computation efficiency.

Uncovering Linguistic Strategies in Kuwaiti Doctor’s Patient-Directed Communication

The current study explores linguistic strategies used by Kuwaiti Doctors in their interactions with patients. By examining the language used in standard doctor-patient interactions, it provides preliminary insights into the communication practices of Kuwaiti healthcare professionals. Due to practical and ethical considerations, twenty-six family doctors participated in a Discourse Completion Task featuring six different scenarios to identify preferred linguistic strategies. The findings reveal a tendency towards indirect requests and questions indicating obligation or possibility when advising patients. Doctors frequently offered help using inclusive first-person plural 'we' inflections, especially with apprehensive patients. In supportive contexts, multiple communicative functions were often utilized simultaneously. These insights are valuable for linguists and healthcare providers, contributing to cross-cultural and inter-language communication research. The findings also inform subsequent investigations into patient preferences, enriching the understanding of effective communication in healthcare settings in Kuwait.

A Review of Algorithms and Platforms for Offloading Decisions in Mobile Cloud Computing

With the substantial growth of mobile applications and the emergence of cloud computing concepts, therefore mobile Cloud Computing (MCC) has been introduced as a potential mobile service technology. Mobile has limited resources, battery life, network bandwidth, storage, and processor, avoid mobile limitations by sending heavy computation to the cloud to get better performance in a short time, the operation of sending data, and get the result of computation call offloading. In this paper, a survey about offloading types is discussed that takes care of many issues such as offloading algorithms, platforms, metrics (that are used with this algorithm and its equations), mobile cloud architecture, and the advantages of using the mobile cloud. The trade-off between local execution of tasks on end-devices and remote execution on the cloud server for minimizing delay time and energy saving. In the form of a multi-objective optimization problem with a focus on reducing overall system power consumption and task execution latency, meta-heuristic algorithms are required to solve this problem which is considered as NP-hardness when the number of tasks is high. To get minimum cost (time and energy) apply partial offloading on specific jobs containing a number of tasks represented in sequences of zeros and ones for example (100111010), when each bit represents a task. The zeros mean the task will be executed in the cloud and the ones mean the task will be executed locally. The decision of processing tasks locally or remotely is important to balance resource utilization. The calculation of task completion time and energy consumption for each task determines which task from the whole job will be executed remotely (been offloaded) and which task will be executed locally. Calculate the total cost (time and energy) for the whole job and determine the minimum total cost. An optimization method based on metaheuristic methods is required to find the best solution. The genetic algorithm is suggested as a metaheuristic Algorithm for future work.

Path Planning Approaches in Multi‐robot System: A Review

ABSTRACTThe essential factor in developing multi‐robot systems is the generation of an optimal path for task completion by multiple robots. To ensure effective path planning, this paper studies the recent publications and provides a detailed review of the path planning approaches to avoid collisions in uncertain environments. In this article, path‐planning approaches for multiple robots are categorized primarily into classical, heuristic, and artificial intelligence‐based methods. Among the heuristic approaches, bio‐inspired approaches are mostly employed to optimize the classical approaches to enhance their adaptability. The articles are analyzed based on static and dynamic scenarios, real‐time experiments, and simulations involving hybrid solutions. The increasing focus on using hybrid approaches in dynamic environments is found mostly in the papers employing heuristic and AI‐based approaches. In real‐time applications, AI‐based approaches are highly implemented in comparison to heuristic and classical approaches. Moreover, the findings from this review, highlighting the strengths and drawbacks of each algorithm, can help researchers select the appropriate approach to overcome the limitations in designing efficient multi‐robot systems.

A Hierarchical Planning Method for AUV Search Tasks Based on the Snake Optimization Algorithm.

In a complex and dynamic battlefield environment, enabling autonomous underwater vehicles (AUVs) to reach dynamic targets in the shortest possible time using global autonomous planning is a key issue affecting the completion of search tasks. In this study, ahierarchicalAUV task planning method that uses a combination of hierarchical programming and a snake optimization algorithm is proposed for two typical cases where the platform can provide initial target information. This method decomposes the search task problem into a three-level programming problem, with the outer task planning goal of achieving the shortest encounter time between AUV and dynamic targets; the goal of task planning in the middle layer is to achieve the shortest actual navigation time for AUVs under different operating conditions; and the internal task planning is responsible for considering the comprehensive trajectory optimization under navigation constraints such as threat zone, path length, and path smoothness. The snake optimization algorithm was used for solving each layer of task planning. The feasibility of the proposed method was verified through simulation experiments of AUV search tasks under two types of initial target information conditions. The simulation results show that this method can achieve task planning for AUV searching for dynamic targets under various constraint conditions, optimize the encounter time between AUV and dynamic targets, and have strong engineering practical value. It has certain reference significance for task planning problems similar to underwater unmanned equipment.